We envision several uses for the present invention. In the fields of elderly care and physical therapy, the present invention finds several important uses. We envision that the invented system can provide both qualitative and quantitative monitoring of an elderly person's physical activity (PA) during his or her everyday life. This information is useful for several reasons: first, PA monitoring can accurately determine the user's state of physical and mental health, identifying subacute changes in their health status. For example, this system can detect early deteriorations in the amount and quality of the subjects' PA due to various health conditions (e.g., congestive heart failure, development of infections, etc.) Second, PA monitoring provides valuable information about the sequence of the elderly person's movements during the time window surrounding their falls. This information significantly aids the development of alert systems to predict, and ideally, prevent fall occurrences. Third, assessment of the effects of new drugs and pain treatments are significantly advanced through monitoring of the subjects' physical activity during his or her everyday life. Fourth, monitoring of PA in the elderly population can, over time, provide insight into qualitative and quantitative changes in PA as a result of all adverse physical events, such as functional declines or hospitalizations. Persons at risk can therefore be identified, and novel preventive interventional methods may be tailored to their needs. The invented system also finds use in remote monitoring and telecare of people suffering from various diseases, such as Alzheimer's, as well as of those recovering and rehabilitating from diseases and medical procedures.
In clinical research and studies, the invented system provides valuable insight into the mechanisms and factors influencing physical activity and balance by quantifying the subject's PA and risk of falling (RoF) in all contexts, including everyday life.
In drug development, the invented system can be used to study the role of various drugs and treatment procedures on the physical activity and RoF of people during clinical studies.
In athletics training, this system provides valuable feedback on the user's body movements, and can be a valuable tool for both training and on-field performance measurement.
Measurement and monitoring of PA by the present invented system also finds use in weight management by providing intelligent feedback to the user about his or her daily energy expenditures.
Postural Transitions:
Najafi et al. [1-3] have developed algorithms for identifying postural transitions (PT), e.g., sit-to-stand (SI-ST) and stand-to-sit (ST-SI) from data recorded by a gyroscopic sensor attached to the subject's trunk. The high power-consumption rates of gyroscopes, however, severely limits the applicability of these algorithms for applications outside of the laboratory (which include everyday life applications), since such a system has an autonomy of only a few hours, therefore requiring frequent recharging or exchanges of the battery. Although the addition of more batteries would increase the device's autonomy, it will also increase its size and weight, thus hindering the subject's natural movements.
By contrast, the algorithms developed as part of the present invention use accelerometer data in place of gyroscope data, and therefore enable long-term, autonomous operability of the system.
Gait Analysis:
Proper gait function (i.e., quality of gait) requires the ability to maintain safe gait while navigating in complex and changing environments, and to conform one's gait to different task demands. Furthermore, a person's quality of gait is closely linked to his or her overall state of health. For example, walking speed correlates with the individual's ability to live independently, with the ability to perform various activities of daily life (such as safely crossing a traffic intersection), and with reductions in the risk of falling [4].
Since evaluation of a person's overall health and quality of life are greatly facilitated by knowledge of his or her gait function during everyday life, a system that can automatically extract gait-related parameters with minimal hindrance of the user's movements is highly useful. To date, however, fully satisfactory methods and systems have not been developed. Current techniques for computing a person's gait parameters are primarily based on the use of vertical accelerometer signals, together with a peak-detection algorithm to identify the walking step. Such techniques, however, possess several important shortcomings.
First, they cannot remove the rotational artifacts generated by the body segment to which the sensor has been attached. These noise artifacts stem from the gravitational component of the accelerometer signal. While they can be easily removed in the case of healthy young subjects, such artifacts pose a key challenge to accurate computation of gait parameters in the case of patients and the elderly—who tend to walk slowly and may use walking aids. Second, current algorithms cannot discriminate between acceleration peaks associated with postural transitions, and those due to walking steps, thus leading to very low specificity during activity daily life (ADL).
Alternative technologies for estimating the gait pattern use combinations of gyroscopes and/or accelerometers attached to the lower limbs [5-7]. Use of gyroscopes decreases the autonomy of the system due to high power consumption. Moreover, attaching the sensors on lower limbs hinders the user's movements, who must carry the system during ADL.
The present invention accurately identifies the user's walking periods during ADL, discriminates between left and right gait steps, and estimates the spatiotemporal parameters of gait (e.g., swing, stance, double support, and gait speed) using only accelerometers. Aminian et al. (1999) [7] have suggested an algorithm, based on a neural network, that extracts spatio-temporal parameters of gait using accelerometers attached to the subject's lower back. This algorithm, however, requires a calibration/pre-learning stage that can only be accomplished by having subjects walk within a constrained space of a gait lab. This requirement renders that algorithm impractical for use during everyday life activities. By contrast, the algorithms developed as part of the present invention require no initial calibrations, and therefore can be easily used by any individual.
In so doing, our algorithms overcome the shortcomings present in the prior art: the small, lightweight and portable sensory module, attached to the subject's chest, poses minimal hindrance to his or her movements during ADL. Furthermore, the accelerometers consume considerably less power than do gyroscopes, leading to significantly longer operational times. Moreover, the invented system provides significantly higher accuracy in discriminations, and better removes rotational noise artifacts.
Risk of Falling:
Evaluation of the individual's risk of falling is required in providing adapted assistance and preventive measures for subjects deemed at a high risk of falling. This risk is generally evaluated by using questionnaires, which have shortcomings such as subjectivity and limited accuracy in recall [8]. Risk of falling can also be evaluated by clinical and functional tests, such as assessments of posture and gait, independence in daily life, cognition, and vision [9-10]. However, an objective method for remotely monitoring this risk through the monitoring the daily physical activity (PA) has not yet been developed. By contrast, the present invention assesses and monitors the user's risk of falling through monitoring and measurement of his or her daily physical activity.
Automatic Fall Detection:
Of the health problems commonly associated with aging, the most serious is falling—defined as a person's trunk, knee, or hand unintentionally coming to rest on the ground or a lower level below the waist. A reliable system to remotely detect falls allows delivery of early care to these persons, and decreases the detrimental consequences of falls, leading to substantial health-care cost savings. Current fall alarm systems require activation and are therefore inappropriate in falls due to syncope, a loss of consciousness associated with cerebro-vascular accidents. Moreover, persons suffering from Alzheimer's disease—affecting approximately one-third of persons aged 80 years and older—may not be capable of activating such systems. A reliable system capable of sending automatic alarms when assistance is necessary will therefore provide an innovative way to support these patients and their caregivers. Automatic fall reporting would also be important in clinical research to reliably record occurrence of falls.
Current detection of falls essentially relies on self-reporting and complex reporting systems with daily phone-call reminders. In fact, for the research community interested in fall prevention, the documentation of falls is a methodological pitfall, and no unanimously accepted method for reporting falls exists. Little data support claims to the reliability and validity of different reporting systems. Oral reports have many limitations due to the cognitive status of the subjects as well as mental factors such as shame or fear of reporting. Finally, fall events associated with loss of consciousness due to syncope, stroke or epileptic seizures are not always recognized.
While a number of different approaches to fall detection have appeared in recent years [11-14], they have primarily used patterns recorded by tri-axial accelerometers to identify shocks related to falls, independent of the previous posture (i.e. sitting, lying, standing) and/or the state of activity (e.g. rest, walking, turning, postural transition, etc) of the faller. Not using the key information about the person's previous posture and state of activity likely gives rise to false detections, dramatically decreasing the accuracy of the fall detector. The present invention, by contrast, identifies falls with high sensitivity and specificity using only signals from accelerometers.